Data Systems Lab

We build the full stack from data systems to autonomous agents. Our engines make data ultrafast to query; our AI databases let people express intent in natural language; our agents ground large language models in enterprise data across text, tables, images, and graphs; and our embodied agents carry the same ideas into robots that act in the physical world. Enterprise knowledge is scattered across text, tables, images, and knowledge graphs. We build retrieval-augmented agents that reason over all of these modalities at once: multi-granular retrievers that fuse table and text evidence, late-interaction retrievers that scale to open-domain multimodal corpora, and agentic traversal strategies that learn from their own failures. We also design principled benchmarks so that progress on complex multi-hop question answering can be measured rigorously. We are redefining what a database engine is in the era of LLMs. CADENZA compiles natural-language intent into task-specific operator DAGs, turning semantic queries into optimizable plans instead of monolithic LLM calls. TurboLynx is the world’s fastest embedded graph DBMS—a schemaless engine for general-purpose analytics over graphs and tables—and Themis executes relational queries on GPUs. Underneath, we keep pushing the core of query optimization: learned cardinality estimation with theoretical guarantees, cost-efficient batching of massive small-query workloads, and cache replacement for direct-access cloud OLAP. Vision-language-action (VLA) models let robots follow open-ended language instructions, but they struggle with what makes a task yours—”bring my cup.” We study how to personalize and ground VLA policies with lightweight visual prompting and in-context adaptation, without retraining the underlying model, and how to conn